Cory Henson, Ph.D.
Knowledge-based artificial intelligence: exploring the synergies between knowledge representation and AI
“The future is already here – it's just not evenly distributed.”
I am a senior research scientist at Bosch Research and Technology Center with a focus on applying knowledge representation and semantic technology to enable autonomous driving. I also hold an Adjunct Faculty position at Wright State University. Prior to joining Bosch, I earned a PhD in Computer Science from WSU, where I worked at the Kno.e.sis Center applying semantic technologies to represent and manage sensor data on the Web.
- Senior research scientist applying KR and semantics for autonomous driving, Bosch Research and Technology Center
- Research scientist developing services to manage sensor data on the Web, Riverside Research
- Ph.D. candidate researching the semantic representation of sensor data, Wright State University
C. Henson (2013)A Semantics-based Approach to Machine Perception
- C. Henson
- Ph.D. Dissertation, Wright State University
C. Henson et al. (2012)An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices
- C. Henson; T. Krishnaprasad; A. Sheth
- International Semantic Web Conference, p. 149-164
C. Henson et al. (2012)Semantic Perception: Converting Sensory Observations to Abstractions
- C. Henson; A. Sheth; T. Krishnaprasad
- IEEE Internet Computing, vol. 16, issue 2, p. 26 - 34
M. Compton et al. (2012)The SSN Ontology of the W3C Semantic Sensor Network Incubator Group
- M. Compton; P. Barnaghi; L. Bermudez; R. Garcia-Castro; O. Corcho; S. Cox; J. Graybeal; M. Hauswirth; C. Henson; A. Herzog; V. Huang; K. Janowicz; W.D. Kelsey; D. Le Phuoc; M. Leggieri; H. Neuhaus; A. Nikolov; K. Taylor
- Journal of Web Semantics, vol. 17
C. Henson et al. (2011)An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web
- C. Henson; T. Krishnaprasad; A. Sheth
- Applied Ontology, vol. 6, issue 4, p. 345-376
C. Henson et al. (2011)Representation of Parsimonious Covering Theory in OWL-DL
- C. Henson; T. Krishnaprasad; A. Sheth; P. Hitzler
- International Workshop on OWL: Experiences and Directions, vol. 796
L. Lefort et al. (2011)Semantic Sensor Network XG Final Report
- L. Lefort; C. Henson; K. Taylor / W3C Incubator Group Report
C. Henson et al. (2009)SemSOS: Semantic sensor Observation Service
- C. Henson; J. Pschorr; A. Sheth; T. Krishnaprasad
- International Symposium on Collaborative Technologies and Systems, p. 44-53
A. Sheth et al. (2008)Semantic Sensor Web
- A. Sheth; C. Henson; S.S. Sahoo
- IEEE Internet Computing, vol. 12, issue 4, p. 78 - 83
Interview with Cory Henson, Ph.D.
Senior Research Scientist focused on the Integration of Knowledge Representation and Machine Learning
Please tell us what fascinates you most about research.
Since childhood, I’ve always wanted to be an explorer, to travel to unknown lands and discover the hidden gems of our world. It’s the reason why I ventured into a career in research, which affords the ability to travel and explore the vast expanse of ideas, searching for unique solutions to exciting challenges.
What makes research done at Bosch so special?
Bosch presents a unique research opportunity, with a long history of developing sensors and sensing systems, with a large collection of sensor data, and with the vision and drive to leverage these resources and know-how to develop the next generation of intelligent systems.
What research topics are you currently working on at Bosch?
AI requires LOTS OF DATA. The good news is that Bosch has lots of data, specifically in the automotive domain. The bad news is that access to this data is elusive. In collaboration with Chassis Systems Control (CC), we are developing knowledge representation and semantic search technologies that will enable data scientists to search and discover the data needed to realize the autonomous driving technologies of the future.
What are the biggest scientific challenges in your field of research?
Knowledge-based artificial intelligence, or KBAI, focuses on finding synergies between knowledge representation technologies and AI. As an example, the use of knowledge graphs to discover and select the features of machine learning algorithms can improve their accuracy, recall, and precision. In the other direction, machine learning may be used to identify additional facts to be asserted within a knowledge graph. This symbiotic, cyclical interaction between these two technologies is often referred to as the virtuous cycle of KBAI, with each improving the other and leading to a next generation of intelligent systems.
How do the results of your research become part of solutions "Invented for life"?
Automated driving will change the future of mobility. There will be fewer traffic jams, lower emissions, and fewer accidents. By 2020 there is expected to be 10 million autonomous vehicles on the road in the United States. This number is expected to rise to 1 in 4 vehicles on the roads being autonomous by 2030*. In order to realize this vision, the vehicle must be able to sense, think, and act in all situations. Such technology, which promises a future with safer driving experiences, is “Invented for life.”